1. Field of the Invention
The present invention relates to an information transfer apparatus, a learning system using the same, an information transfer method, and a computer-readable recording medium for realizing them. In particular, the present invention relates to a technique for selectively transferring information used in learning processing for generating a learning model in an image analysis system using a machine learning technique.
2. Background Art
In recent years, surveillance cameras have rapidly become widespread. Surveillance cameras are installed across a vast area, e.g., on the streets, in stores and public facilities, and on highways. Due to the recent improvements in the performances of solid-state image sensors, the resolution of surveillance cameras is increasing. The bands necessary for image transmission are becoming broader as well.
A conventional surveillance system will now be described with reference to FIG. 16. FIG. 16 shows a schematic configuration of a conventional surveillance system utilizing a broadband network. As shown in FIG. 16, in a surveillance system 400, images from surveillance cameras 401 are transmitted to a surveillance server 403 via a network 402. The surveillance server 403 is installed in a location where an observer 404 is stationed, such as a surveillance room, and accumulates the transmitted images. The surveillance server 404 also displays the accumulated images on a screen of a terminal 406 of the observer 404.
This allows the observer 403 to perform surveillance by viewing the screen. A detection server 405 may be connected to the surveillance server 403. The detection server 405 has an automatic surveillance function, and can detect an intruder from the images (for example, see JP 2012-88787A). Thus, the use of the detection server 405 supports surveillance by the observer 404 and reduces the surveillance burden on the observer 404.
However, intensive surveillance of images from high-resolution cameras that are distributed across a vast area requires a broadband network (NW) as shown in FIG. 16, and is not practical as it increases the cost for constructing a surveillance system.
In view of this, a surveillance system has been suggested in which a surveillance camera itself has an automatic surveillance function, the surveillance camera alone performs automatic surveillance based on some sort of detection model, and a notification is issued to a surveillance room and the like only on the occurrence of an event serving as a surveillance target.
FIG. 17 shows a schematic configuration of a conventional surveillance system using surveillance cameras with an automatic surveillance function. In a surveillance system 500 shown in FIG. 17, surveillance cameras 501 issue a notification only on the occurrence of an abnormality, and thus a network 502 need not be a broadband network.
When each surveillance camera 501 itself has the automatic surveillance function as in an example shown in FIG. 17, it is critical for the surveillance cameras 501 to update their respective detection models to keep the detection models in a normal state. This is because, if no action is taken in response to an environmental change and a change in a surveillance target on an as-needed basis, erroneous detection and incomplete detection increase, and effective automatic surveillance cannot be continued.
It is possible to adopt a mode in which the surveillance cameras 501 update their respective detection models on an individual basis. However, it is difficult to adopt this mode because it is difficult for a single surveillance camera to secure resources necessary for updating a detection model. Another reason why it is difficult to adopt this mode is because updating a detection model requires external information for specifying a change in a surveillance target, as well as images from another surveillance camera. The detection models may be updated by human hand in accordance with the states of the surveillance cameras 501. However, an increase in the number of surveillance cameras would render this method unpractical in terms of workload and time.
In view of this, in the example shown in FIG. 17, a model update server 505 that updates the detection models of the surveillance cameras 501 is installed in addition to a surveillance server 503. The model update server 505 generates, for each surveillance camera, a detection model corresponding to an environmental change and a change in a surveillance target through learning of training data using a machine learning technique. The model update server 505 transfers the generated detection models to the surveillance cameras 501 to cause the surveillance cameras 501 to update their respective detection models. In FIG. 17, 504 denotes an observer, and 506 denotes a terminal of the observer.
However, in the surveillance system shown in FIG. 17, as the band of the network is limited, there is a restriction on data that can be transmitted from the surveillance cameras 501 to the model update server 505. Therefore, the surveillance system shown in FIG. 17 encounters the problem that the model update server 505 cannot accumulate sufficient training data, and it is difficult to dynamically update the detection models.
It is presumed that this problem can be solved by selectively transmitting only data that is worth learning to the model update server 505 in accordance with the band of the network. However, in the conventional surveillance system shown in FIG. 17, the surveillance camera 501 does not have such a function, and thus it is difficult to selectively transmit data that is worth learning.